Current State of and Future Opportunities for Prediction in Microbiome Research: Report from the Mid-Atlantic Microbiome Meet-up in Baltimore on 9 January 2019

Eric Sakowski^#¹, Gherman Uritskiy^#², Rachel Cooper³, Maya Gomes⁴, Michael R McLaren⁵, Jacquelyn S Meisel⁶, Rebecca L Mickol⁷, C David Mintz⁸, Emmanuel F Mongodin⁹, Mihai Pop⁶, Mohammad Arifur Rahman¹⁰, Alvaro Sanchez¹¹, Winston Timp¹², Jeseth Delgado Vela¹³, Carly Muletz Wolz¹⁴, Joseph P Zackular¹⁵, Jessica Chopyk¹⁶, Seth Commichaux⁶, Meghan Davis¹⁷, Douglas Dluzen¹⁸, Sukirth M Ganesan¹⁹, Muyideen Haruna¹⁸, Dan Nasko⁶, Mary J Regan²⁰, Saul Sarria⁶, Nidhi Shah⁶, Brook Stacy⁶, Dylan Taylor⁶, Jocelyne DiRuggiero²¹, Sarah P Preheim²²

Affiliations

¹ Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
² Department of Biology, Johns Hopkins University, Baltimore, Maryland, USA.
³ Molecular and Comparative Pathobiology, Johns Hopkins University, Baltimore, Maryland, USA.
⁴ Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, Maryland, USA.
⁵ Department of Population Health and Pathobiology, North Carolina State University, Raleigh, North Carolina, USA.
⁶ Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA.
⁷ American Society for Engineering Education, Washington, DC, USA.
⁸ Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
⁹ University of Maryland School of Medicine, Institute for Genome Sciences, Baltimore, Maryland, USA.
¹⁰ Department of Computer Science, George Mason University, Fairfax, Virginia, USA.
¹¹ Department of Ecology and Evolutionary Biology, Yale University, New Haven Connecticut, USA.
¹² Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
¹³ Department of Civil and Environmental Engineering, Howard University, Washington, DC, USA.
¹⁴ Center for Conservation Genomics, Smithsonian National Zoological Park & Conservation Biology Institute, Washington, DC, USA.
¹⁵ Department of Pathology and Laboratory Medicine, University of Pennsylvania and Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
¹⁶ School of Public Health, University of Maryland, College Park, Maryland, USA.
¹⁷ Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
¹⁸ Department of Biology, Morgan State University, Baltimore, Maryland, USA.
¹⁹ National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, Maryland, USA.
²⁰ University of Maryland School of Nursing, Baltimore, Maryland, USA.
²¹ Department of Biology, Johns Hopkins University, Baltimore, Maryland, USA jdiruggiero@jhu.edu sprehei1@jhu.edu.
²² Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland, USA jdiruggiero@jhu.edu sprehei1@jhu.edu.

^# Contributed equally.

PMID: 31594828
PMCID: PMC6787564
DOI: 10.1128/mSystems.00392-19

Current State of and Future Opportunities for Prediction in Microbiome Research: Report from the Mid-Atlantic Microbiome Meet-up in Baltimore on 9 January 2019

Eric Sakowski et al. mSystems. 2019.

. 2019 Oct 8;4(5):e00392-19.

doi: 10.1128/mSystems.00392-19.

Authors

Affiliations

¹ Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
² Department of Biology, Johns Hopkins University, Baltimore, Maryland, USA.
³ Molecular and Comparative Pathobiology, Johns Hopkins University, Baltimore, Maryland, USA.
⁴ Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, Maryland, USA.
⁵ Department of Population Health and Pathobiology, North Carolina State University, Raleigh, North Carolina, USA.
⁶ Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA.
⁷ American Society for Engineering Education, Washington, DC, USA.
⁸ Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
⁹ University of Maryland School of Medicine, Institute for Genome Sciences, Baltimore, Maryland, USA.
¹⁰ Department of Computer Science, George Mason University, Fairfax, Virginia, USA.
¹¹ Department of Ecology and Evolutionary Biology, Yale University, New Haven Connecticut, USA.
¹² Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
¹³ Department of Civil and Environmental Engineering, Howard University, Washington, DC, USA.
¹⁴ Center for Conservation Genomics, Smithsonian National Zoological Park & Conservation Biology Institute, Washington, DC, USA.
¹⁵ Department of Pathology and Laboratory Medicine, University of Pennsylvania and Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
¹⁶ School of Public Health, University of Maryland, College Park, Maryland, USA.
¹⁷ Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
¹⁸ Department of Biology, Morgan State University, Baltimore, Maryland, USA.
¹⁹ National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, Maryland, USA.
²⁰ University of Maryland School of Nursing, Baltimore, Maryland, USA.
²¹ Department of Biology, Johns Hopkins University, Baltimore, Maryland, USA jdiruggiero@jhu.edu sprehei1@jhu.edu.
²² Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland, USA jdiruggiero@jhu.edu sprehei1@jhu.edu.

^# Contributed equally.

PMID: 31594828
PMCID: PMC6787564
DOI: 10.1128/mSystems.00392-19

Abstract

Accurate predictions across multiple fields of microbiome research have far-reaching benefits to society, but there are few widely accepted quantitative tools to make accurate predictions about microbial communities and their functions. More discussion is needed about the current state of microbiome analysis and the tools required to overcome the hurdles preventing development and implementation of predictive analyses. We summarize the ideas generated by participants of the Mid-Atlantic Microbiome Meet-up in January 2019. While it was clear from the presentations that most fields have advanced beyond simple associative and descriptive analyses, most fields lack essential elements needed for the development and application of accurate microbiome predictions. Participants stressed the need for standardization, reproducibility, and accessibility of quantitative tools as key to advancing predictions in microbiome analysis. We highlight hurdles that participants identified and propose directions for future efforts that will advance the use of prediction in microbiome research.

Keywords: bioinformatics; conceptual models; machine learning; metagenomics; microbiome; prediction; quantitative models.

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Figures

**FIG 1**
Self-reported positions of registered M³ attendees for the 2019 meeting.

**FIG 2**
Geographic distribution of M³ participants drawn from registration information.

**FIG 3**
Breakdown of M³ participant affiliation with institutions listed.

See this image and copyright information in PMC

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Current State of and Future Opportunities for Prediction in Microbiome Research: Report from the Mid-Atlantic Microbiome Meet-up in Baltimore on 9 January 2019

Affiliations

Current State of and Future Opportunities for Prediction in Microbiome Research: Report from the Mid-Atlantic Microbiome Meet-up in Baltimore on 9 January 2019

Authors

Affiliations

Abstract

Figures

References

LinkOut - more resources

Full Text Sources